from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

reporting = Reporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.022	0.0	-1	100	NaN	NaN	0.001	0.0	0.271	0.0	See	See
2	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.030	0.0	1	100	NaN	NaN	0.000	0.0	0.612	0.0	See	See
4	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.029	0.0	-1	1	NaN	NaN	0.000	0.0	0.648	0.0	See	See
6	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.030	0.0	1	5	NaN	NaN	0.000	0.0	0.624	0.0	See	See
8	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.029	0.0	1	1	NaN	NaN	0.000	0.0	0.620	0.0	See	See
10	KNeighborsClassifier_brute_force	fit	100	100	10	NaN	0.0	0.0	0.028	0.0	-1	5	NaN	NaN	0.000	0.0	0.662	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.004	0.001	0.0	0.004	-1	100	0.0	1.0	0.0	0.0	26.483	17.440	See	See
3	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.001	0.000	0.0	0.001	1	100	0.0	1.0	0.0	0.0	3.622	2.196	See	See
5	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.003	0.000	0.0	0.003	-1	1	1.0	0.0	0.0	0.0	24.394	16.597	See	See
7	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.001	0.000	0.0	0.001	1	5	1.0	0.0	0.0	0.0	4.165	2.966	See	See
9	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.001	0.000	0.0	0.001	1	1	1.0	1.0	0.0	0.0	4.078	2.756	See	See
11	KNeighborsClassifier_brute_force	predict	100	1	10	NaN	0.004	0.001	0.0	0.004	-1	5	1.0	1.0	0.0	0.0	22.282	13.428	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	100	100	10	16	0.045	0.0	0.003	0.0	k-means++	NaN	15	NaN	0.001	0.0	35.797	0.0	See	See
2	KMeans_tall	fit	100	100	10	7	0.004	0.0	0.014	0.0	random	NaN	8	NaN	0.001	0.0	4.518	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	100	1	10	16	0.002	0.0	0.001	0.002	k-means++	1.0	15	1.0	0.0	0.0	11.333	6.713	See	See
3	KMeans_tall	predict	100	1	10	7	0.002	0.0	0.000	0.002	random	1.0	8	1.0	0.0	0.0	11.421	6.753	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=50, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	100	100	10	3	0.004	0.0	0.006	0.0	random	NaN	4	NaN	0.001	0.0	2.915	0.0	See	See
2	KMeans_short	fit	100	100	10	3	0.012	0.0	0.002	0.0	k-means++	NaN	3	NaN	0.001	0.0	13.002	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	100	1	10	3	0.002	0.0	0.0	0.002	random	1.0	4	1.0	0.0	0.0	10.531	5.830	See	See
3	KMeans_short	predict	100	1	10	3	0.002	0.0	0.0	0.002	k-means++	1.0	3	1.0	0.0	0.0	10.950	6.738	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	100	100	10	[11]	0.003	0.0	[0.02727495]	0.0	NaN	NaN	NaN	NaN	NaN	0.002	0.0	1.319	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	100	1	10	[11]	0.0	0.0	[0.01167073]	0.0	NaN	NaN	NaN	NaN	1.0	0.0	0.0	0.407	0.361	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	100	100	10	NaN	0.001	0.0	0.01	0.0	NaN	NaN	NaN	0.001	0.0	1.262	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	100	1	10	NaN	0.0	0.0	0.001	0.0	NaN	NaN	NaN	0.0	0.0	0.597	0.607	See	See